
import numpy as np   
from sklearn.linear_model import LinearRegression
from scipy import stats 
from scipy.stats import zscore
from scipy.cluster.hierarchy import dendrogram, linkage
from scipy.cluster.hierarchy import fcluster
from sklearn.cluster import KMeans 
from sklearn.metrics import silhouette_samples, silhouette_score
import pandas as pd    
import matplotlib.pyplot as plt 
import seaborn as sns
%matplotlib inline 
sns.set(color_codes=True)
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
from sklearn.model_selection import train_test_split


#Loading the json file and concatinating it with car names
ca=pd.read_json (r'./Part1+-+Car-Attributes.json')
ca1=pd.read_csv('./Part1+-+Car+name.csv')
car=pd.concat([ca,ca1],axis=1)
car.head()


row, column = car.shape
print('The dataset contains', row, 'rows and', column, 'columns')

The dataset contains 398 rows and 9 columns


# save this data as to csv,xlsx and json
car.to_csv('mpg.csv', index=False)
car.to_excel('mpg.xlsx', index = False)
car.to_json('mpg.json', orient = 'split', compression = 'infer', index = 'true')


#dropping/ignoring car_name 
car = car.drop('car_name', axis=1)
# Also replacing the categorical var with actual values
car['origin'] = car['origin'].replace({1: 'america', 2: 'europe', 3: 'asia'})
car.head()


print('The data type of each attribute: \n')
car.info()

The data type of each attribute: 

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 398 entries, 0 to 397
Data columns (total 8 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   mpg     398 non-null    float64
 1   cyl     398 non-null    int64  
 2   disp    398 non-null    float64
 3   hp      398 non-null    object 
 4   wt      398 non-null    int64  
 5   acc     398 non-null    float64
 6   yr      398 non-null    int64  
 7   origin  398 non-null    object 
dtypes: float64(3), int64(3), object(2)
memory usage: 25.0+ KB


#5-point summary
car.describe().T


# isdigit()? on 'horsepower' 
hpIsDigit = pd.DataFrame(car.hp.str.isdigit()) 

#print isDigit = False!
car[hpIsDigit['hp'] == False]


# Replace missing values with NaN
car = car.replace('?', np.nan)
car[hpIsDigit['hp'] == False]


#replace the missing values with median value. 
car.median()

mpg       23.0
cyl        4.0
disp     148.5
hp        93.5
wt      2803.5
acc       15.5
yr        76.0
dtype: float64


#replace hp with median
car['hp'].fillna((car['hp'].median()), inplace=True)


print('The data set has no missing values \n')
car.isnull().sum()

The data set has no missing values

mpg       0
cyl       0
disp      0
hp        0
wt        0
acc       0
yr        0
origin    0
dtype: int64


#creating mpg_level attribute using the 25% and 75% of mpg values( <  17 as low, > 17 and <29 as medium, > 29 as high  )
car['mpg_level'] = car['mpg'].apply(lambda x: 'low' if x<17 else 'high' if x>29 else 'medium')
car.head()


#categorical variable
car_cat = car.iloc[:,[1,6,7,8]]
car_cat.head()


#numeric variables
car_num=car.drop(['cyl','yr','origin','mpg_level'],axis=1)
car_num.head()


#plotting categorical variables
fig = plt.figure(1, (14, 8))

for i,car in enumerate(car_cat.columns):
    ax = plt.subplot(2,2,i+1)
    sns.countplot(car_cat[car], order=car_cat[car].value_counts().index)
    ax.set_xlabel(None)
    ax.set_title(f'Distribution of {car}')
    plt.tight_layout()

plt.show()


#plot histograms
car_num.hist(bins = 20, figsize = (10, 8), color = 'blue')
plt.show()


#plot density
plt.figure(figsize=(17, 13))
col = 1
for i in car_num.columns:
    plt.subplot(3, 3, col)
    sns.distplot(car_num[i], color = 'b')
    col += 1


#joining the categorical and numerical variables
car=pd.concat([car_cat,car_num],axis=1)


#checking for attribute type
car.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 398 entries, 0 to 397
Data columns (total 9 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   cyl        398 non-null    int64  
 1   yr         398 non-null    int64  
 2   origin     398 non-null    object 
 3   mpg_level  398 non-null    object 
 4   mpg        398 non-null    float64
 5   disp       398 non-null    float64
 6   hp         398 non-null    float64
 7   wt         398 non-null    int64  
 8   acc        398 non-null    float64
dtypes: float64(4), int64(3), object(2)
memory usage: 28.1+ KB


#create dummy varibale for mpg_level and origin and creating a seperate datset for k-means and heirarchical clustering
car = pd.get_dummies(car, columns=['origin'])
car = pd.get_dummies(car, columns=['mpg_level'])
car.head()
carH=car.copy()
carK=car.copy()


#pair plot for the numeric attributes
car_attr = car.iloc[:, 0:7]
sns.pairplot(car_attr, diag_kind='kde');


#dropping the created dummy variable
car2=car.drop(['origin_america','origin_asia','origin_europe','mpg_level_high','mpg_level_low','mpg_level_medium'],axis=1)


#checking of outliers
plt.figure(figsize=(25, 20))
col = 1
for i in car2.columns:
    plt.subplot(3, 3, col)
    sns.boxplot(car2[i],color='blue')
    col += 1


#replacing outliers with IQR (Q1 and Q3 +-1.5*IQR)
IQR1 = stats.iqr(car2['hp'], interpolation = 'midpoint')
IQR2 = stats.iqr(car2['acc'], interpolation = 'midpoint')


#Horsepower after imputing outliers
Q3 = car2['hp'].quantile(0.75)
car2['hp'] = np.where(car2["hp"] >(Q3+1.5*IQR1), 198.5,car2['hp'])
sns.boxplot(car2['hp']);


#accelaration after imputing outliers
Q1 = car2['acc'].quantile(0.25)
Q31=car2['acc'].quantile(0.75)
car2['acc'] = np.where(car2["acc"] >(Q31+1.5*IQR2),22.10 ,car2['acc'])
car2['acc'] = np.where(car2["acc"] <(Q1-1.5*IQR2),(Q1-1.5*IQR2),car2['acc'])
sns.boxplot(car2['acc']);


#checking for correlation
plt.figure(figsize=(10,8))
corr=car2.corr()
sns.heatmap(corr,annot=True);


#separating numeric variables
cc = car.iloc[:,0:7] 
cc.head()


#scaling the variable
cc_z = cc.apply(zscore)
cc_z.head()


#calculating pairwise distance using average linkage method
link_method = linkage(cc_z.iloc[:,0:7], method = 'average')


#plotting the H-cluster
plt.figure(figsize=(25, 10))
dendrogram(link_method)
plt.show()


# dendrogram function to arrive at dendrogram
dendrogram(
    link_method,
    truncate_mode='lastp',  
    p=2,  
)
plt.show()


#vieweing the clusters formed
clusters = fcluster(link_method, 2, criterion='maxclust')
clusters

array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1,
       1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2,
       2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 1, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2,
       2, 1, 2, 1, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1,
       1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1,
       1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
       2, 2], dtype=int32)


#attaching the clusters formed to the scales data
cc_z['clusters_H'] = clusters
cc_z.head()


#vieweing the distribution of clusters
cc_z.clusters_H.value_counts().sort_index()

1    100
2    298
Name: clusters_H, dtype: int64


#attaching the clusters formed to the original data
cc['clusters_H']=clusters
carH['clusters_H']=clusters
cc.head()


#create a new data set named Hclus
Hclus=cc
Hclus.head()


#aggregating the numerical variable with the clusters formed with the mean
aggdata=cc.iloc[:,0:8].groupby('clusters_H').mean()
aggdata['Freq']=cc.clusters_H.value_counts().sort_index()
aggdata


#plotting the clusters formed
plt.figure(figsize=(10, 8))
sns.scatterplot(x="mpg", y="hp", hue="clusters_H",
              data=cc_z, 
                    palette=['green','brown']);


#seperating the numeric values
cc = car.iloc[:,0:7] 
cc_z1 = cc.apply(zscore)
cc_z1.head()


#calculatint the within sum of squares
wss =[] 
for i in range(1,5):
    KM = KMeans(n_clusters=i)
    KM.fit(cc_z1)
    wss.append(KM.inertia_)
wss

[2785.9999999999995, 1294.841895072732, 946.0197908553794, 738.3743876111234]


#plotting the WSS against the number of cluster to come up with optimal number of clusters using Elbow-method
plt.plot(range(1,5), wss);
plt.title('Elbow Method');
plt.xlabel("Number of Clusters")
plt.ylabel("WSS");


#using 2 centroids for clustering
k_means = KMeans(n_clusters = 2)
k_means.fit(cc_z1)
labels = k_means.labels_


# Calculating silhouette_score
silhouette_score(cc_z1,labels)

0.48235946103916116


#calculating silhouette score for different centroids
kmeans_kwargs = {
   "init": "random",
   "n_init": 10,
   "max_iter": 300,
   "random_state": 42,
}


silhouette_coefficients = []

 # Notice you start at 2 clusters for silhouette coefficient
for k in range(2, 7):
    kmeans = KMeans(n_clusters=k, **kmeans_kwargs)
    kmeans.fit(cc_z1)
    score = silhouette_score(cc_z1,kmeans.labels_)
    silhouette_coefficients.append(score)


#plotting silhouette score for different centroids
plt.plot(range(2, 7), silhouette_coefficients)
plt.xticks(range(2, 7))
plt.xlabel("Number of Clusters")
plt.ylabel("Silhouette Coefficient")
plt.show()


#attaching the labels to the datasets
cc["cluster_K"] = labels
carK['cluster_K']=labels
Kclus=cc
Kclus.head()


#vieweing the distribution of clusters
cc.cluster_K.value_counts().sort_index()

0    293
1    105
Name: cluster_K, dtype: int64


#attaching the clsuters to the scaled data
cc_z1["cluster_K"] = labels
cc_z1.head()


#aggregating the numerical variable with the clusters formed with the mean
aggdata=cc.iloc[:,0:8].groupby('cluster_K').mean()
aggdata['Freq']=cc.cluster_K.value_counts().sort_index()
aggdata


#plotting the clusters
plt.figure(figsize=(10, 8))
sns.scatterplot(x="mpg", y="hp", hue="cluster_K",
              data=cc_z1, 
                    palette=['green','brown']);


carH.clusters_H.value_counts().sort_index()

1    100
2    298
Name: clusters_H, dtype: int64


carK.cluster_K.value_counts().sort_index()

0    293
1    105
Name: cluster_K, dtype: int64


carH.shape

(398, 14)


carK.shape

(398, 14)


car.head()


X = car.drop(['mpg','origin_europe','mpg_level_low'], axis=1)
# the dependent variable
y = car[['mpg']]


# Split X and y into training and test set in 70:30 ratio

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=15)


regression_model = LinearRegression()
regression_model.fit(X_train, y_train)

LinearRegression()


for idx, col_name in enumerate(X_train.columns):
    print("The coefficient for {} is {}".format(col_name, regression_model.coef_[0][idx]))

The coefficient for cyl is -0.5134441386218828
The coefficient for yr is 0.4434650429116842
The coefficient for disp is 0.010688858394646887
The coefficient for hp is 0.010315514536314008
The coefficient for wt is -0.004538788568737129
The coefficient for acc is 0.19183425608862537
The coefficient for origin_america is -1.7306209513688993
The coefficient for origin_asia is -0.8976724344009405
The coefficient for mpg_level_high is 8.552374663817027
The coefficient for mpg_level_medium is 1.5941218694850492


intercept = regression_model.intercept_[0]
print("The intercept for our model is {}".format(intercept))

The intercept for our model is -1.6635717568652169


regression_model.score(X_train, y_train)

0.8967703023839786


O=regression_model.score(X_test, y_test)
O

0.9037421476349174


#renaming the cluster labels to light and heavy vehicles and creating dummy variables of it
carK['cluster_K']=carK['cluster_K'].astype('category')
carK['cluster_K'] = carK['cluster_K'].replace({1: 'heavy', 0: 'light'})
carK = pd.get_dummies(carK, columns=['cluster_K'])


carK.head()


X = carK.drop(['mpg','origin_europe','mpg_level_low','cluster_K_light'], axis=1)
# the dependent variable
y = carK[['mpg']]


# Split X and y into training and test set in 70:30 ratio

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=12)


regression_model = LinearRegression()
regression_model.fit(X_train, y_train)

LinearRegression()


for idx, col_name in enumerate(X_train.columns):
    print("The coefficient for {} is {}".format(col_name, regression_model.coef_[0][idx]))

The coefficient for cyl is -1.1945995644777805
The coefficient for yr is 0.43186510415059826
The coefficient for disp is 0.017477496279110098
The coefficient for hp is -0.010138045835905891
The coefficient for wt is -0.0040684301693864056
The coefficient for acc is 0.1856482874624993
The coefficient for origin_america is -1.6918315494304086
The coefficient for origin_asia is -0.7407779192303001
The coefficient for mpg_level_high is 9.28312093915688
The coefficient for mpg_level_medium is 2.25000171423125
The coefficient for cluster_K_heavy is 2.511514014338475


intercept = regression_model.intercept_[0]
print("The intercept for our model is {}".format(intercept))

The intercept for our model is 1.2041468067173469


regression_model.score(X_train, y_train)

0.8942370456543635


K=regression_model.score(X_test, y_test)
K

0.9117893808052382


#renaming the cluster labels to light and heavy vehicles and creating summy variable of it
carH['clusters_H']=carH['clusters_H'].astype('category')
carH['clusters_H'] = carH['clusters_H'].replace({1: 'heavy', 2: 'light'})
carH = pd.get_dummies(carH, columns=['clusters_H'])


X = carH.drop(['mpg','origin_europe','mpg_level_low','clusters_H_light'], axis=1)
# the dependent variable
y = carH[['mpg']]


# Split X and y into training and test set in 70:30 ratio

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=10)


regression_model = LinearRegression()
regression_model.fit(X_train, y_train)

LinearRegression()


for idx, col_name in enumerate(X_train.columns):
    print("The coefficient for {} is {}".format(col_name, regression_model.coef_[0][idx]))

The coefficient for cyl is -1.0104832432576671
The coefficient for yr is 0.4475417357550161
The coefficient for disp is 0.015115200524614403
The coefficient for hp is -0.013301584387234493
The coefficient for wt is -0.00426417978067245
The coefficient for acc is 0.11805139164484575
The coefficient for origin_america is -2.1174569315391154
The coefficient for origin_asia is -1.3974915348558108
The coefficient for mpg_level_high is 8.565948239298274
The coefficient for mpg_level_medium is 1.6577250698582813
The coefficient for clusters_H_heavy is 2.038974468807404


intercept = regression_model.intercept_[0]
print("The intercept for our model is {}".format(intercept))

The intercept for our model is 2.572729318233023


regression_model.score(X_train, y_train)

0.8988409890950728


H=regression_model.score(X_test, y_test)
H

0.9010238373846703


modellists = []
modellists.append(['Linear Regression on Original Data set', O*100])
modellists.append(['Linear Regression with K means clusters', K*100])
modellists.append(['Linear Regression with Heirarchical clusters', H*100])
mdl_df = pd.DataFrame(modellists, columns = ['Model','r^2 on Test'])
mdl_df


from sklearn.decomposition import PCA
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.metrics import confusion_matrix, classification_report,accuracy_score
from sklearn.metrics import precision_recall_fscore_support
from sklearn.metrics import confusion_matrix
from sklearn import metrics


#import the dataset
ve = pd.read_csv('./vehicle.csv')
ve.head()


#checking for the dimension of the data
rows, column = ve.shape
print('The dataset contains', rows, 'rows and', column, 'columns.')

The dataset contains 846 rows and 19 columns.


#checking for the data type
print('The data type of each attribute: \n')
ve.info()

The data type of each attribute: 

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 846 entries, 0 to 845
Data columns (total 19 columns):
 #   Column                       Non-Null Count  Dtype  
---  ------                       --------------  -----  
 0   compactness                  846 non-null    int64  
 1   circularity                  841 non-null    float64
 2   distance_circularity         842 non-null    float64
 3   radius_ratio                 840 non-null    float64
 4   pr.axis_aspect_ratio         844 non-null    float64
 5   max.length_aspect_ratio      846 non-null    int64  
 6   scatter_ratio                845 non-null    float64
 7   elongatedness                845 non-null    float64
 8   pr.axis_rectangularity       843 non-null    float64
 9   max.length_rectangularity    846 non-null    int64  
 10  scaled_variance              843 non-null    float64
 11  scaled_variance.1            844 non-null    float64
 12  scaled_radius_of_gyration    844 non-null    float64
 13  scaled_radius_of_gyration.1  842 non-null    float64
 14  skewness_about               840 non-null    float64
 15  skewness_about.1             845 non-null    float64
 16  skewness_about.2             845 non-null    float64
 17  hollows_ratio                846 non-null    int64  
 18  class                        846 non-null    object 
dtypes: float64(14), int64(4), object(1)
memory usage: 125.7+ KB


print('Duplicated rows: ', ve[ve.duplicated()].shape[0])

Duplicated rows:  0


print('Null values:\n', ve.isnull().sum())

Null values:
 compactness                    0
circularity                    5
distance_circularity           4
radius_ratio                   6
pr.axis_aspect_ratio           2
max.length_aspect_ratio        0
scatter_ratio                  1
elongatedness                  1
pr.axis_rectangularity         3
max.length_rectangularity      0
scaled_variance                3
scaled_variance.1              2
scaled_radius_of_gyration      2
scaled_radius_of_gyration.1    4
skewness_about                 6
skewness_about.1               1
skewness_about.2               1
hollows_ratio                  0
class                          0
dtype: int64


#replacing the missing values with median values
for cols in ve.columns:
    if(cols != 'class'): 
        ve[cols] = ve[cols].fillna(ve[cols].median())


#5-point summary
ve.describe().T


#plotting the distribution of the numerical variables
ve.hist(bins = 20, figsize = (20, 18), color = 'blue')
plt.show()


#Density plots
plt.figure(figsize=(20, 18))
col = 1
for i in ve.drop(columns='class').columns:
    plt.subplot(4, 5, col)
    sns.distplot(ve[i], color = 'b')
    col += 1


#checking the distribution of the class variable
print(ve['class'].value_counts())
plt.title('Count of Vehicle Class column')
sns.countplot(x = 'class', data = ve);

car    429
bus    218
van    199
Name: class, dtype: int64


#boxplots for outliers
plt.figure(figsize=(25,23))
col = 1
for i in ve.drop(columns='class').columns:
    plt.subplot(6, 4, col)
    sns.boxplot(ve[i],color='blue')
    col += 1


#Boxplots against the class with other numeric variables
fig,axs = plt.subplots(4,5,figsize=(20,18))
sns.boxplot(x="class", y="compactness",data=ve,ax=axs[0,0]);
sns.boxplot(x="class", y="circularity",data=ve,ax=axs[0,1]);
sns.boxplot(x="class", y="distance_circularity",data=ve,ax=axs[0,2]);
sns.boxplot(x="class", y="radius_ratio",data=ve,ax=axs[0,3]);
sns.boxplot(x="class", y="pr.axis_aspect_ratio",data=ve,ax=axs[0,4]);
sns.boxplot(x="class", y="max.length_aspect_ratio",data=ve,ax=axs[1,0]);
sns.boxplot(x="class", y="scatter_ratio",data=ve,ax=axs[1,1]);
sns.boxplot(x="class", y="elongatedness",data=ve,ax=axs[1,2]);
sns.boxplot(x="class", y="pr.axis_rectangularity",data=ve,ax=axs[1,3]);
sns.boxplot(x="class", y="max.length_rectangularity",data=ve,ax=axs[1,4]);
sns.boxplot(x="class", y="scaled_variance",data=ve,ax=axs[2,0]);
sns.boxplot(x="class", y="scaled_variance.1",data=ve,ax=axs[2,1]);
sns.boxplot(x="class", y="scaled_radius_of_gyration",data=ve,ax=axs[2,2]);
sns.boxplot(x="class", y="scaled_radius_of_gyration.1",data=ve,ax=axs[2,3]);
sns.boxplot(x="class", y="skewness_about",data=ve,ax=axs[2,4]);
sns.boxplot(x="class", y="skewness_about.1",data=ve,ax=axs[3,0]);
sns.boxplot(x="class", y="skewness_about.2",data=ve,ax=axs[3,1]);
sns.boxplot(x="class", y="hollows_ratio",data=ve,ax=axs[3,2]);
fig.tight_layout()


#find the outliers and replace them by median
for col_name in ve.drop(columns = 'class').columns:
    q1 = ve[col_name].quantile(0.25)
    q3 = ve[col_name].quantile(0.75)
    iqr = q3 - q1
    
    low = q1 - 1.5 * iqr
    high = q3 + 1.5 * iqr
    
    ve.loc[(ve[col_name] < low) | (ve[col_name] > high), col_name] = ve[col_name].median()


#boxplot after outlier treatment
plt.figure(figsize=(25,23))
col = 1
for i in ve.drop(columns='class').columns:
    plt.subplot(6, 4, col)
    sns.boxplot(ve[i],color='blue')
    col += 1


#checking for correlation
plt.figure(figsize=(20,18))
corr=ve.corr()
sns.heatmap(corr,annot=True);


#spliiting the data to check for correlation with the class variable
X = ve.loc[:, ve.columns != 'class']
y = ve['class'].astype('category').cat.codes


#plotting the correlation with target variable
plt.figure(figsize = (15, 8))
ax=sns.barplot(x=X.columns, y = X.corrwith(y))
sns.barplot(x = X.columns, y = X.corrwith(y))
plt.title('Correlation with Class column', fontsize = 20)
x=plt.setp(ax.get_xticklabels(), rotation=90)


#scaling the muerica variables
XScaled=X.apply(zscore)
XScaled.head()


#plotting the cummulative variance explained by the principal componets
pca = PCA()
X_pca_ = pca.fit_transform(XScaled)
plt.figure(figsize = (12, 8))
plt.plot((np.cumsum(pca.explained_variance_ratio_) * 100), marker = 'X')
plt.xlim(0, 18)
plt.xlabel('Number of Components')
plt.ylabel('Percentage of Cumulative Explained Variance');


print(pca.explained_variance_)

[9.74940269e+00 3.35071912e+00 1.19238155e+00 1.13381916e+00
 8.83997312e-01 6.66265745e-01 3.18150910e-01 2.28179142e-01
 1.31018595e-01 7.98619108e-02 7.33979478e-02 6.46162669e-02
 4.01448646e-02 3.22758478e-02 2.93936408e-02 2.27005257e-02
 1.98136761e-02 5.16287320e-03]


#plotting the
plt.figure(figsize = (12, 8))
plt.step(list(range(18)), (np.cumsum(pca.explained_variance_ratio_) * 100), where = 'mid')
plt.xlim(0, 18)
plt.xlabel('Number of Components')
plt.ylabel('Percentage of Cumulative Explained Variance')
plt.title('Vehicle Dataset Explained Variance');


#Using 6 components and printing the eigen vectors
pca3 = PCA(n_components=6)
pca3.fit(XScaled)
print(pca3.components_)
print(pca3.explained_variance_ratio_)
Xpca3 = pca3.transform(XScaled)

[[ 0.27250289  0.28725469  0.30242111  0.26971354  0.09786073  0.19520014
   0.31052393 -0.3090069   0.307287    0.27815416  0.29976509  0.30553237
   0.26323762 -0.04193594  0.03608321  0.05872048  0.03801314  0.08474   ]
 [-0.08704358  0.13162176 -0.04614301 -0.19793126 -0.25783995 -0.10804563
   0.07528535 -0.01322994  0.0875602   0.12215424  0.07726575  0.07150302
   0.21058205  0.50362158 -0.01576632 -0.09274624 -0.50162122 -0.50761211]
 [-0.03818521 -0.20114691  0.06346211  0.05628517 -0.06199275 -0.14895782
   0.10904283 -0.09085269  0.1060705  -0.21368469  0.1445998   0.11034374
  -0.20287019  0.07386402 -0.55917399  0.6706805  -0.06224071 -0.04170535]
 [ 0.13867501 -0.03805548  0.10895429 -0.25435509 -0.61276572  0.27867816
   0.00539295  0.06521486  0.03089915  0.04146747 -0.06400509 -0.00219687
  -0.08553965 -0.11539962  0.47370331  0.42842603 -0.0274096   0.09603749]
 [ 0.13710147 -0.13899555 -0.08001743  0.13374437  0.12360146 -0.63489336
   0.08555745 -0.07907344  0.08164638 -0.25111294  0.14747123  0.11010098
  -0.00521211  0.1380686   0.56655224  0.13086982  0.18051929 -0.11078807]
 [ 0.26361138 -0.07134741 -0.01690062 -0.13818366 -0.57782861 -0.289097
   0.09774711 -0.07572829  0.10540323 -0.07819622  0.13291241  0.11539822
  -0.0670574  -0.13151308 -0.31917609 -0.46840497  0.28013644  0.05944441]]
[0.54099325 0.18593103 0.06616512 0.0629155  0.04905291 0.03697101]


#printing the original features and the reduced features
pca_6 = PCA(n_components = 6)
X_pca = pca_6.fit_transform(XScaled)
print('Original number of features:', X.shape[1])
print('Reduced number of features:', X_pca.shape[1])

Original number of features: 18
Reduced number of features: 6


#viewing the first 5 observations of the pca components
pca_df = pd.DataFrame(data = X_pca)
pca_df.head()


sns.pairplot(pca_df, diag_kind = 'kde');


#splitting the original data into train and test 70:30
X_train, X_test, y_train, y_test = train_test_split(XScaled, y, test_size = 0.3, random_state = 10)


rtr, ctr = X_train.shape
print('The training set comprises of', rtr, 'rows and', ctr, 'columns.')

The training set comprises of 592 rows and 18 columns.


rt, ct = X_test.shape
print('The test set comprises of', rt, 'rows and', ct, 'columns.')

The test set comprises of 254 rows and 18 columns.


#splitting the pca data into train and test 70:30
X_tr, X_te, y_tr, y_te = train_test_split(X_pca, y, test_size = 0.3, random_state = 10)


rtr_pca, ctr_pca = X_tr.shape
print('The PCA training set comprises of', rtr_pca, 'rows and', ctr_pca, 'columns.')

The PCA training set comprises of 592 rows and 6 columns.


rt_pca, ct_pca = X_te.shape
print('The PCA test set comprises of', rt_pca, 'rows and', ct_pca, 'columns.')

The PCA test set comprises of 254 rows and 6 columns.


# Building a Support Vector Machine on train data
svc_model = SVC(C= 4, kernel='rbf', gamma='scale')
svc_model.fit(X_train, y_train)

SVC(C=4)


#predicting on train data
sv_train_predict = svc_model .predict(X_train)
print("Model Accuracy on train: {0:.4f}".format(metrics.accuracy_score(y_train, sv_train_predict)))
print()

Model Accuracy on train: 0.9899


#predicting on test data
sv_test_predict = svc_model .predict(X_test)
print("Model Accuracy on test: {0:.4f}".format(metrics.accuracy_score(y_test, sv_test_predict)))
print()

Model Accuracy on test: 0.9685


#visualization of confusion matrix in the form of a heatmap
cm= confusion_matrix(y_test, sv_test_predict)
plt.figure(figsize = (12, 8))
sns.heatmap(cm, annot = True, cmap = 'RdYlGn', fmt = 'd')
plt.xlabel('Actual Classes', fontsize = 15)
plt.ylabel('Predicted Classes', fontsize = 15)
plt.title('Confusion Matrix for SVM', fontsize = 15);


#printing classification report
print("Classification Report")
print(metrics.classification_report(y_test, sv_test_predict, labels=[0,1,2]))

Classification Report
              precision    recall  f1-score   support

           0       1.00      0.99      0.99        71
           1       0.98      0.96      0.97       125
           2       0.90      0.97      0.93        58

    accuracy                           0.97       254
   macro avg       0.96      0.97      0.97       254
weighted avg       0.97      0.97      0.97       254


precision_SV, recall_SV, f1_score_SV, support = precision_recall_fscore_support(y_test, sv_test_predict,average='macro')
print('Precision Score :', '%0.2f' % precision_SV)
print('Recall Score :', '%0.2f' % recall_SV)
print('F1-Score:', '%0.2f' % f1_score_SV)
SV_Acc= accuracy_score(y_test, sv_test_predict)
print('Accuracy Score :','%0.2f' % SV_Acc)

Precision Score : 0.96
Recall Score : 0.97
F1-Score: 0.97
Accuracy Score : 0.97


#SVM on the pca data
svc_model_pca = SVC(C= 4, kernel='rbf', gamma='scale')
svc_model_pca.fit(X_tr, y_tr)

SVC(C=4)


#predicting on train data
sv_tr_predict = svc_model_pca .predict(X_tr)
print("Model Accuracy on train: {0:.4f}".format(metrics.accuracy_score(y_tr, sv_tr_predict)))
print()

Model Accuracy on train: 0.9476


#predicting on test data
sv_te_predict = svc_model_pca .predict(X_te)
print("Model Accuracy on test: {0:.4f}".format(metrics.accuracy_score(y_te, sv_te_predict)))
print()

Model Accuracy on test: 0.9213


#visualization of confusion matrix in the form of a heatmap
cm= confusion_matrix(y_te, sv_te_predict)
plt.figure(figsize = (12, 8))
sns.heatmap(cm, annot = True, cmap = 'RdYlGn', fmt = 'd')
plt.xlabel('Actual Classes', fontsize = 15)
plt.ylabel('Predicted Classes', fontsize = 15)
plt.title('Confusion Matrix for SVM', fontsize = 15);


#printing classification report
print("Classification Report")
print(metrics.classification_report(y_te, sv_te_predict, labels=[0,1,2]))

Classification Report
              precision    recall  f1-score   support

           0       0.96      0.94      0.95        71
           1       0.94      0.91      0.93       125
           2       0.84      0.91      0.88        58

    accuracy                           0.92       254
   macro avg       0.91      0.92      0.92       254
weighted avg       0.92      0.92      0.92       254


precision_SV_pca, recall_SV_pca, f1_score_SV_pca, support_pca = precision_recall_fscore_support(y_te, sv_te_predict,average='macro')
print('Precision Score :', '%0.2f' % precision_SV_pca)
print('Recall Score :', '%0.2f' % recall_SV_pca)
print('F1-Score:', '%0.2f' % f1_score_SV_pca)
SV_Acc_pca= accuracy_score(y_te, sv_te_predict)
print('Accuracy Score :','%0.2f' % SV_Acc_pca)

Precision Score : 0.91
Recall Score : 0.92
F1-Score: 0.92
Accuracy Score : 0.92


modellists = []
modellists.append(['Support Vector Classifier without PCA', SV_Acc * 100, recall_SV * 100, precision_SV * 100,f1_score_SV*100])
modellists.append(['Support Vector Classifier with PCA', SV_Acc_pca* 100, recall_SV_pca * 100, precision_SV_pca * 100,f1_score_SV_pca*100])
mdl_df = pd.DataFrame(modellists, columns = ['Model','Accuracy Score of Test Data', 'Recall Score', 'Precision Score','F1 Score'])
mdl_df

	mpg	cyl	disp	hp	wt	acc	yr	origin	car_name
0	18.0	8	307.0	130	3504	12.0	70	1	chevrolet chevelle malibu
1	15.0	8	350.0	165	3693	11.5	70	1	buick skylark 320
2	18.0	8	318.0	150	3436	11.0	70	1	plymouth satellite
3	16.0	8	304.0	150	3433	12.0	70	1	amc rebel sst
4	17.0	8	302.0	140	3449	10.5	70	1	ford torino

	count	mean	std	min	25%	50%	75%	max
mpg	398.0	23.514573	7.815984	9.0	17.500	23.0	29.000	46.6
cyl	398.0	5.454774	1.701004	3.0	4.000	4.0	8.000	8.0
disp	398.0	193.425879	104.269838	68.0	104.250	148.5	262.000	455.0
wt	398.0	2970.424623	846.841774	1613.0	2223.750	2803.5	3608.000	5140.0
acc	398.0	15.568090	2.757689	8.0	13.825	15.5	17.175	24.8
yr	398.0	76.010050	3.697627	70.0	73.000	76.0	79.000	82.0

	mpg	disp	hp	wt	acc
0	18.0	307.0	130.0	3504	12.0
1	15.0	350.0	165.0	3693	11.5
2	18.0	318.0	150.0	3436	11.0
3	16.0	304.0	150.0	3433	12.0
4	17.0	302.0	140.0	3449	10.5

	cyl	yr	mpg	disp	hp	wt	acc
0	8	70	18.0	307.0	130.0	3504	12.0
1	8	70	15.0	350.0	165.0	3693	11.5
2	8	70	18.0	318.0	150.0	3436	11.0
3	8	70	16.0	304.0	150.0	3433	12.0
4	8	70	17.0	302.0	140.0	3449	10.5

	cyl	yr	mpg	disp	hp	wt	acc
0	1.498191	-1.627426	-0.706439	1.090604	0.673118	0.630870	-1.295498
1	1.498191	-1.627426	-1.090751	1.503514	1.589958	0.854333	-1.477038
2	1.498191	-1.627426	-0.706439	1.196232	1.197027	0.550470	-1.658577
3	1.498191	-1.627426	-0.962647	1.061796	1.197027	0.546923	-1.295498
4	1.498191	-1.627426	-0.834543	1.042591	0.935072	0.565841	-1.840117

Part A¶

PART B¶

	mpg	cyl	disp	hp	wt	acc	yr	origin
32	25.0	4	98.0	?	2046	19.0	71	america
126	21.0	6	200.0	?	2875	17.0	74	america
330	40.9	4	85.0	?	1835	17.3	80	europe
336	23.6	4	140.0	?	2905	14.3	80	america
354	34.5	4	100.0	?	2320	15.8	81	europe
374	23.0	4	151.0	?	3035	20.5	82	america

	cyl	yr	origin	mpg_level
0	8	70	america	medium
1	8	70	america	low
2	8	70	america	medium
3	8	70	america	low
4	8	70	america	medium

	cyl	yr	mpg	disp	hp	wt	acc	Freq
clusters_H
1	7.980000	73.740000	14.684000	345.470000	160.400000	4121.560000	12.702000	100
2	4.607383	76.771812	26.477852	142.404362	85.479866	2584.137584	16.529866	298

	cyl	yr	mpg	disp	hp	wt	acc	Freq
cluster_K
0	4.569966	76.822526	26.619113	140.250853	85.061433	2567.860068	16.535836	293
1	7.923810	73.742857	14.851429	341.809524	158.000000	4093.771429	12.867619	105

	Model	r^2 on Test
0	Linear Regression on Original Data set	90.374215
1	Linear Regression with K means clusters	91.178938
2	Linear Regression with Heirarchical clusters	90.102384

	compactness	circularity	distance_circularity	radius_ratio	pr.axis_aspect_ratio	max.length_aspect_ratio	scatter_ratio	elongatedness	pr.axis_rectangularity	max.length_rectangularity	scaled_variance	scaled_variance.1	scaled_radius_of_gyration	scaled_radius_of_gyration.1	skewness_about	skewness_about.1	skewness_about.2	hollows_ratio	class
0	95	48.0	83.0	178.0	72.0	10	162.0	42.0	20.0	159	176.0	379.0	184.0	70.0	6.0	16.0	187.0	197	van
1	91	41.0	84.0	141.0	57.0	9	149.0	45.0	19.0	143	170.0	330.0	158.0	72.0	9.0	14.0	189.0	199	van
2	104	50.0	106.0	209.0	66.0	10	207.0	32.0	23.0	158	223.0	635.0	220.0	73.0	14.0	9.0	188.0	196	car
3	93	41.0	82.0	159.0	63.0	9	144.0	46.0	19.0	143	160.0	309.0	127.0	63.0	6.0	10.0	199.0	207	van
4	85	44.0	70.0	205.0	103.0	52	149.0	45.0	19.0	144	241.0	325.0	188.0	127.0	9.0	11.0	180.0	183	bus

	count	mean	std	min	25%	50%	75%	max
compactness	846.0	93.678487	8.234474	73.0	87.00	93.0	100.00	119.0
circularity	846.0	44.823877	6.134272	33.0	40.00	44.0	49.00	59.0
distance_circularity	846.0	82.100473	15.741569	40.0	70.00	80.0	98.00	112.0
radius_ratio	846.0	168.874704	33.401356	104.0	141.00	167.0	195.00	333.0
pr.axis_aspect_ratio	846.0	61.677305	7.882188	47.0	57.00	61.0	65.00	138.0
max.length_aspect_ratio	846.0	8.567376	4.601217	2.0	7.00	8.0	10.00	55.0
scatter_ratio	846.0	168.887707	33.197710	112.0	147.00	157.0	198.00	265.0
elongatedness	846.0	40.936170	7.811882	26.0	33.00	43.0	46.00	61.0
pr.axis_rectangularity	846.0	20.580378	2.588558	17.0	19.00	20.0	23.00	29.0
max.length_rectangularity	846.0	147.998818	14.515652	118.0	137.00	146.0	159.00	188.0
scaled_variance	846.0	188.596927	31.360427	130.0	167.00	179.0	217.00	320.0
scaled_variance.1	846.0	439.314421	176.496341	184.0	318.25	363.5	586.75	1018.0
scaled_radius_of_gyration	846.0	174.706856	32.546277	109.0	149.00	173.5	198.00	268.0
scaled_radius_of_gyration.1	846.0	72.443262	7.468734	59.0	67.00	71.5	75.00	135.0
skewness_about	846.0	6.361702	4.903244	0.0	2.00	6.0	9.00	22.0
skewness_about.1	846.0	12.600473	8.930962	0.0	5.00	11.0	19.00	41.0
skewness_about.2	846.0	188.918440	6.152247	176.0	184.00	188.0	193.00	206.0
hollows_ratio	846.0	195.632388	7.438797	181.0	190.25	197.0	201.00	211.0

	compactness	circularity	distance_circularity	radius_ratio	pr.axis_aspect_ratio	max.length_aspect_ratio	scatter_ratio	elongatedness	pr.axis_rectangularity	max.length_rectangularity	scaled_variance	scaled_variance.1	scaled_radius_of_gyration	scaled_radius_of_gyration.1	skewness_about	skewness_about.1	skewness_about.2	hollows_ratio
0	0.160580	0.518073	0.057177	0.300945	1.933135	0.912212	-0.207598	0.136262	-0.224342	0.758332	-0.400771	-0.337407	0.285705	-0.315806	-0.032330	0.387162	-0.312012	0.183957
1	-0.325470	-0.623732	0.120741	-0.850666	-0.740596	0.427456	-0.599423	0.520519	-0.610886	-0.344578	-0.594220	-0.618623	-0.513630	0.009122	0.624090	0.161740	0.013265	0.452977
2	1.254193	0.844303	1.519141	1.265808	0.863642	0.912212	1.148719	-1.144597	0.935290	0.689401	1.114582	1.131806	1.392477	0.171586	1.718123	-0.401818	-0.149374	0.049447
3	-0.082445	-0.623732	-0.006386	-0.290423	0.328896	0.427456	-0.750125	0.648605	-0.610886	-0.344578	-0.916635	-0.739145	-1.466683	-1.453054	-0.032330	-0.289106	1.639649	1.529056
4	-1.054545	-0.134387	-0.769150	1.141310	-0.027601	-0.057300	-0.599423	0.520519	-0.610886	-0.275646	1.694930	-0.647319	0.408680	-0.072110	0.624090	-0.176395	-1.450481	-1.699181

	0	1	2	3	4	5
0	0.584228	-0.675673	-0.453334	-0.750656	-0.777515	-1.848809
1	-1.512180	-0.348934	-0.333436	1.268953	-0.324929	-0.118317
2	3.913448	0.234507	-1.265094	0.137224	0.915751	-0.685594
3	-1.535193	-3.044413	-0.469623	0.324317	-0.611590	0.367777
4	-0.642062	1.488882	-0.246288	-0.550939	0.471655	-1.012698

	Model	Accuracy Score of Test Data	Recall Score	Precision Score	F1 Score
0	Support Vector Classifier without PCA	96.850394	97.047758	96.227745	96.596702
1	Support Vector Classifier with PCA	92.125984	92.315169	91.352049	91.773898